RNA-seq Quality Control

RNA-seq specific QC metrics beyond general read quality.

rRNA Contamination Detection

High rRNA content indicates failed rRNA depletion or polyA selection.

SortMeRNA

sortmerna \
    --ref rRNA_databases/smr_v4.3_default_db.fasta \
    --reads sample.fastq.gz \
    --aligned rRNA_reads \
    --other non_rRNA_reads \
    --fastx \
    --threads 8

rrna_count=$(grep -c "^@" rRNA_reads.fastq 2>/dev/null || echo 0)
total_count=$(zcat sample.fastq.gz | grep -c "^@")
rrna_pct=$(echo "scale=2; $rrna_count / $total_count * 100" | bc)
echo "rRNA: ${rrna_pct}%"

BLAST Against rRNA

seqkit sample -n 10000 sample.fastq.gz | seqkit fq2fa > sample_10k.fasta
blastn -query sample_10k.fasta -db rrna_db -outfmt 6 -evalue 1e-10 -max_target_seqs 1 | wc -l

Expected rRNA Levels

Strandedness Verification

RSeQC infer_experiment

infer_experiment.py -i aligned.bam -r genes.bed

Output Interpretation

Fraction of reads explained by "1++,1--,2+-,2-+": 0.9856  # Forward stranded
Fraction of reads explained by "1+-,1-+,2++,2--": 0.0144  # Reverse (should be low)

Strand Inference

Salmon Strandedness

salmon quant -i index -l A -r sample.fastq.gz -o quant/
grep "library_types" quant/lib_format_counts.json

Gene Body Coverage

Check for 3′ or 5′ bias indicating RNA degradation.

RSeQC geneBody_coverage

geneBody_coverage.py \
    -i aligned.bam \
    -r housekeeping_genes.bed \
    -o coverage

Interpretation

Read Distribution

RSeQC read_distribution

read_distribution.py -i aligned.bam -r genes.bed > distribution.txt

Expected Distribution

Transcript Integrity Number (TIN)

Measure of RNA degradation per transcript.

RSeQC tin

tin.py -i aligned.bam -r genes.bed > tin_scores.txt

TIN Interpretation

Duplication Rate

Picard MarkDuplicates

java -jar picard.jar MarkDuplicates \
    I=aligned.bam \
    O=marked.bam \
    M=dup_metrics.txt \
    REMOVE_DUPLICATES=false

grep -A 1 "LIBRARY" dup_metrics.txt | tail -1 | cut -f9

RNA-seq Expected Duplication

Insert Size (Paired-End)

Picard CollectInsertSizeMetrics

java -jar picard.jar CollectInsertSizeMetrics \
    I=aligned.bam \
    O=insert_metrics.txt \
    H=insert_histogram.pdf

Saturation Analysis

Subsampling Analysis

for frac in 0.1 0.25 0.5 0.75 1.0; do
    samtools view -bs $frac aligned.bam > sub_${frac}.bam
    featureCounts -a genes.gtf -o counts_${frac}.txt sub_${frac}.bam
    detected=$(awk '$7 > 0' counts_${frac}.txt | wc -l)
    echo "$frac: $detected genes"
done

Picard CollectRnaSeqMetrics

Comprehensive RNA-seq metrics from Picard.

java -jar picard.jar CollectRnaSeqMetrics \
    I=aligned.bam \
    O=rnaseq_metrics.txt \
    REF_FLAT=refFlat.txt \
    STRAND=SECOND_READ_TRANSCRIPTION_STRAND \
    RIBOSOMAL_INTERVALS=rRNA.interval_list

Key Metrics

MultiQC Report

Aggregate all QC metrics.

multiqc fastqc/ star_output/ featurecounts/ -o multiqc_report/

Complete RNA-seq QC Pipeline

#!/bin/bash
SAMPLE=$1
BAM=$2
GENES_BED=$3
REF_FLAT=$4

echo "=== RNA-seq QC: $SAMPLE ===" > qc_report.txt

echo -e "\n--- Strandedness ---" >> qc_report.txt
infer_experiment.py -i $BAM -r $GENES_BED >> qc_report.txt

echo -e "\n--- Read Distribution ---" >> qc_report.txt
read_distribution.py -i $BAM -r $GENES_BED >> qc_report.txt

echo -e "\n--- Gene Body Coverage ---" >> qc_report.txt
geneBody_coverage.py -i $BAM -r $GENES_BED -o coverage

echo -e "\n--- TIN Scores ---" >> qc_report.txt
tin.py -i $BAM -r $GENES_BED > tin.txt
awk '{sum+=$3; count++} END {print "Mean TIN:", sum/count}' tin.txt >> qc_report.txt

echo -e "\n--- Duplication ---" >> qc_report.txt
java -jar picard.jar MarkDuplicates I=$BAM O=/dev/null M=dup.txt 2>/dev/null
grep -A 1 "LIBRARY" dup.txt | tail -1 | awk '{print "Duplication rate:", $9}' >> qc_report.txt

echo -e "\n--- RNA-seq Metrics ---" >> qc_report.txt
java -jar picard.jar CollectRnaSeqMetrics I=$BAM O=rnaseq.txt REF_FLAT=$REF_FLAT STRAND=SECOND_READ_TRANSCRIPTION_STRAND 2>/dev/null
grep -A 2 "## METRICS CLASS" rnaseq.txt >> qc_report.txt

cat qc_report.txt

Python QC Summary

import pysam
import numpy as np
from collections import Counter

def rnaseq_qc(bam_file, sample_size=100000):
    bam = pysam.AlignmentFile(bam_file, 'rb')

    strand_counts = Counter()
    insert_sizes = []

    for i, read in enumerate(bam.fetch()):
        if i >= sample_size:
            break
        if not read.is_unmapped:
            if read.is_read1:
                if read.is_reverse:
                    strand_counts['1-'] += 1
                else:
                    strand_counts['1+'] += 1
            if read.is_proper_pair and read.template_length > 0:
                insert_sizes.append(read.template_length)

    bam.close()

    total = sum(strand_counts.values())
    print(f'Read 1 forward: {strand_counts["1+"]/total:.2%}')
    print(f'Read 1 reverse: {strand_counts["1-"]/total:.2%}')

    if insert_sizes:
        print(f'Median insert: {np.median(insert_sizes):.0f}')

rnaseq_qc('aligned.bam')

QC Thresholds Summary

Related Skills

• quality-reports – General FastQC
• fastp-workflow – Read trimming
• alignment-files/alignment-validation – General BAM QC
• rna-quantification/featurecounts-counting – Quantification after QC